Image mapping is carried out for many purposes such as image compression, contrast enhancement, and for enabling images captured with capture devices of particular types to be displayed using display devices of different capabilities. For example, in the field of medical imaging (or in other fields such as professional photography, robotic imaging systems, high dynamic range photography, depth cameras, capture devices often produce 16-bit images where for example, each pixel may be one of 65,536 levels of grey (in the case of a greyscale image). Other image capture devices may produce 12-bit images or 32-bit images depending on the image capture device. The term “bit-depth” is used to refer to the number of bits available per pixel at an image capture or display device.
Where images have been captured with high bit-depth devices it is often required to reduce the bit-depth to enable the captured images to be displayed on a display device with lower bit-depth. This is difficult to achieve whilst preserving as much information as possible, so as not to lose the original dynamic range captured in the high bit-depth device. This is important for many types of images and particularly so in the field of medical imaging, where images often have particularly high dynamic range and where it is required to visualize fine details in images and remove noise as far as possible in order to make accurate medical diagnoses.
Dynamic range of an image may be thought of as the ratio between the intensities of the brightest and darkest recordable parts of that image. Tone-mapping functions are typically used to compress the dynamic range of an image to allow more detail in the original image to be visualized on a display whilst preferably preserving the “natural look” of the image. Improved tone-mapping systems are required which may produce more realistic, useful results in a computationally inexpensive, fast and robust manner.
Where images are captured at devices with relatively high bit-depth, it is often required to compress those captured images to reduce their size for storage and/or transmission. Image compression is difficult to achieve in a manner which is computationally inexpensive, fast, which does not produce visible artefacts and which is reversible (that is, the original image can be obtained from the compressed image without loss of quality).
Previous approaches for mapping images of one bit-depth to another bit-depth have included histogram equalization, linear mappings and gamma mappings. Linear mappings and gamma mappings are straightforward techniques but which are also very limited in the quality of results they give.
Histogram equalization tone-mapping processes typically involve taking the cumulative histogram of an image to be tone-mapped. The cumulative histogram is then normalized to 255 (in the case that the output bit-depth is 8 bits) and the normalized cumulative histogram is then used as a mapping function to transform the original image to the required bit-depth. However, histogram equalization processes are often found to be very aggressive and as a result fine details in images are lost. Artefacts may also be introduced such as gradient reversal and quantization or banding artefacts.
Local histogram equalization tone-mapping processes are also known. These are sometimes referred to as adaptive histogram equalizing techniques. They involve applying different transforms to equalize the histograms of sub-regions of an image. These approaches are typically highly computationally intensive and difficult to implement in real-time applications. Noise and artefacts may also be introduced for example, because a rectangular window is typically used around each pixel for histogram equalization in this window. The resulting transform is then not spatially smooth.
Previously, a single sigmoid function has been used as a tone-mapping function, with the sigmoid function determined from original image statistics and taking perceptual preference guidelines into account. Taking subjective preferences into account allows the image to look as pleasing as possible to the viewer. This is desirable in consumer imaging and commercial photography. However, this approach is not suitable for medical imaging applications, satellite imaging, archiving and the like where the goal is information preservation. In such cases it is required to preserve or enhance details at all regions and luminance levels of an image, not just those suited to the human visual system.
The embodiments described below are not limited to implementations which solve any or all of the noted disadvantages of known image mapping systems.